Book a demoBook a demo

Compare ChatGPT, Claude, Gemini against Starbridge for public sector sales teams

Run 4 prompts yourself to compare with Starbridge.

Comparison scorecard
ChatGPTClaudeGemini
Contract dataFrom FOIA requests including incumbent vendors, expiration dates and contract valuesView test 1YesNo
Verified public-sector contactsBounce-checked and continuously updatedView test 2YesLimited
Pre-RFP buying signalsFind opportunities before the RFP within board meeting minutes, new budgets, job changes and moreView test 3YesLimited
CRM integrationsNative bidirectional sync with configurable fields, run conditions, and audit loggingView test 4YesPartial
Starbridge MCPAccess public sector sales intelligence directly in any AI toolSee how it worksYesYes

Based on running the prompts below in ChatGPT as of September 2026. Tools change, so run them yourself.

Don’t take our word for it. Run the prompts.

Test 1: Contract and purchase order data coverage

The prompt

For this list of 50 cities, please tell me who their current ERP is, when that contract expires, and how much the contract is worth. Use only verified contract data from public records.

The list of 50 cities is added automatically when you run or copy the prompt.

Run in ChatGPT

ChatGPT

ChatGPT’s answer to the ERP contract prompt, after working for 1 minute 25 seconds: a table of cities where the verified ERP vendor, expiration date and contract value read not verified or a dash for New York, Los Angeles, Chicago, Atlanta, Washington DC and Miami, with only Dallas and Houston filled in

Starbridge competitor presence table of city contracts, including New York City, Los Angeles, Chicago, Dallas, Houston, Washington DC, Miami and Philadelphia, with each vendor (CGI, Oracle, Emburse, Workday, SAP), estimated annual contract value and estimated contract end date

Why Starbridge contract data is better than general AI

  • A general AI assistant can only answer from what it can read on the open web.
  • SLED contract data isn’t openly available, centralized, or clean.
  • Much of it is only available through highly manual FOIA requests (Freedom of Information Act).
  • Starbridge collects and cleans those contract records ahead of time, so the incumbent vendor, expiration date, and contract terms are ready when a rep asks.

Test 2: Verified public-sector contacts

The prompt

Please give me the emails for all of the CIOs of the cities below. Only use official public sources like .gov and .edu domains to ensure accuracy.

The list of 50 cities is added automatically when you run or copy the prompt.

Run in ChatGPT

ChatGPT

ChatGPT table of city CIOs where the officially published email column reads not publicly listed or not found for Los Angeles, Chicago, Dallas, Washington DC, Miami, Philadelphia, Phoenix and New York

Starbridge contacts table of eleven verified chief information officers at major U.S. cities such as Los Angeles, Chicago and New York City, each with job title, city and direct email address

Why Starbridge contact data is better than general AI

  • Direct CIO emails often aren’t public. City sites publish only a department inbox, phone number, or contact form, and legacy platforms, JavaScript rendering, and PDFs keep an LLM from scraping the rest.
  • Starbridge reads agency directories, board minutes, PDFs, and verified .gov and .edu domains, then fills gaps through a waterfall of 8 enrichment providers.
  • Every contact is verified: a validator confirms the person works at the agency, and a bounce-check confirms the email is live. The result is 98% contact accuracy, kept current in your CRM.

Test 3: Pre-RFP buying signals

The prompt

For this list of 50 cities, please tell me what the central themes of their council meetings have been in the last 90 days. Use only verified transcripts from their board meetings and always include an attributable link to those meetings. If you cannot access a board meeting directly, do not guess.

The list of 50 cities is added automatically when you run or copy the prompt.

Run in ChatGPT

ChatGPT

ChatGPT’s answer on city council meeting themes: it found readable transcripts for only 5 of 50 cities, so its findings table lists themes for Philadelphia, Boston, Denver, Sacramento and Austin and shows dashes for every other city

Claude answer using Starbridge MCP on council meeting themes: it found transcripts, minutes and agendas for 48 cities, a bar shows 48 of 50 city entries with at least one meeting source link, and a table lists 50 U.S. cities such as New York, Los Angeles and Chicago with their central themes and links to meeting sources

Why Starbridge can spot more buying signals before the RFP

  • Public documents in government and education are scattered and difficult to read: they sit in PDFs, dated websites and YouTube videos.
  • Starbridge drills into links within links, which is necessary to surface every record for a public-sector account.
  • A central database of public documents is expensive to maintain for a single company. Starbridge spreads that cost across all its customers.

Test 4: CRM Integrations (i.e. Salesforce)

The prompt

What are the pros and cons of ChatGPT Salesforce integration vs. Starbridge integration?

Run in ChatGPT

ChatGPT

ChatGPT table comparing Starbridge and ChatGPT Salesforce integrations across primary job, external intelligence, CRM enrichment, continuous sync, signals, pipeline analysis, and public-sector specialization

Starbridge Salesforce connection marked Synced, on the Objects and fields tab: the Account object is switched on, and its optional fields such as account city, state code, type, URL, zip code, Census IDs, NCES and IPEDS IDs and Starbridge Buyer ID are all checked to sync

Why general AI should be used alongside Starbridge for Salesforce

Starbridge puts public-sector data into Salesforce
  • Without the core data like FOIA’d contracts, contacts and public documents, there is no data for ChatGPT to analyze
  • Native bidirectional sync with configurable fields, run conditions, and audit logging
  • Deterministic safe guards to prevent overwriting sensitive data
  • Brings budgets, board minutes, contracts, grants, RFPs, and job changes into the CRM
ChatGPT reasons over what Salesforce already holds
  • Works from CRM records, but doesn’t inherently have data like FOIA’d contracts and contacts
  • Rates its own public-sector specialization as “essentially none without Starbridge or other context”

Use ChatGPT to reason over your CRM. Use Starbridge to fill it with the public-sector data worth reasoning over.

Starbridge MCP

Already using ChatGPT or Claude? Bring Starbridge into it.

The Starbridge MCP connects Claude and ChatGPT to live public sector intelligence. Research accounts, map competitors, and surface active RFPs in seconds.

  • Map competitor deployments and open whitespace across your territory
  • Generate a full account briefing, with buying signals and verified contacts, before any meeting
  • Find active RFPs and competitor contracts expiring in the next 90 days, then draft the outreach
See how Starbridge MCP works

See which public-sector buyers are ready to buy what you sell

Scan your site in seconds. Starbridge identifies your ICP and surfaces emerging opportunities from board meetings, job changes, new grants, contract expirations, RFPs, and more.

See your top accounts now